On the sensitivity of RSS based localization using the log-normal model: An empirical study

The accuracy of radial radio propagation models, e.g. the log-normal path loss model, is severely degraded by the effects of multipath propagation, environmental differences and hardware variability. This has a direct impact on the performance of node localization algorithms that use these models. In this paper, first we study the effect of the environment and hardware variability on the model parameters of the log-normal path loss model. We empirically show that, even in the same environment, the model parameters can vary significantly depending on the nodes used for the calibration. Second, we present node localization results obtained using a maximum likelihood algorithm and evaluate the sensitivity of the algorithm to model parameter changes. Third, we show that the localization results can be improved using an individual model for each node. Using a robot for nodes localization, we report experimental results in three different environments: an open sports hall, a semi-open lobby, and a cluttered office. In corresponding order, accuracy of 0.33 m, 1.07 m and 0.78 m is achieved using individual models.